A SCOPING REVIEW OF SYSTEMS-LEVEL INTERVENTIONS FOR FALL PREVENTION IN LONG-TERM CARE

Abstract Older adults residing in long-term care facilities (LTCFs) are at high risk for falls. In addition to individual resident-level interventions to prevent falls and fall-related injury (FRI), system-focused interventions are also necessary to adequately address fall prevention. Relatively little attention has been given to research involving systems-focused interventions. We aimed to synthesize studies on the effects of system-focused interventions for fall and FRI prevention in LTCFs with the goal of identifying promising strategies and gaps. We searched Medline, CINAHL, and EMBASE databases from 2017 to 2022 following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guideline. We conducted a narrative synthesis to summarize included studies. In the initial review, 301 studies were identified and underwent title and abstract screening resulting in 98 articles were retrieved for full-text review. Fifteen studies were included in data extraction. Fall prevention interventions evaluated in LTCF settings include: (1) multicomponent fall prevention programs, (2) staff education programs, (3) training in safe handling/transfers, (4) environmental adaptations, (5) electronic health record algorithms and prompts, (6) local culture change, (7) video analysis of falls to change local interventions, and (8) facility-level physical activity programs. About half of reported studies reported significant effects of system-focused interventions to reduce fall and FRI in LTCFs. Multicomponent fall prevention programs and physical activity interventions are the most effective systems-level interventions, while electronic health record interventions were the least effective. Overall, little attention has been given in the literature to evaluation of environmental adaptations at the systems level.

p<.01) was observed across the 3.3-month span of season 1.Following the 4-month summer break, level of caregiver distress at the outset of season 2 had increased back to initial baseline levels (ZBI =19.9 units -a moderate to borderlinehigh level), thereby underscoring the importance of the choir intervention.These findings suggest that lifestyle engagement may offer an effective complement for lessening the impact of caregiver distress for CP, with implications for the development of dementia care plans.

HOW CAREGIVERS DIVIDE RESPONSIBILITIES: A LATENT CLASS ANALYSIS OF US CAREGIVING ARRANGEMENTS Erin Ice, University of Michigan, Ann Arbor, Michigan, United States
Family and other unpaid caregivers provide the bulk of caregiving for older adults, but we know little about how kin divide responsibilities amongst themselves, and how they share tasks with paid caregivers.This study identifies the common "care configurations" that emerge to support older adults with functional limitations.Studying older adults (age 65+) who received assistance from a caregiver in the National Health and Aging Trends Study Wave 5 (n = 4,182), I generate an extensive battery of caregiving arrangement indicators, including household, physical care, transportation, and administrative assistance provided by spouses, children, other unpaid kin, or paid caregivers.I use latent class analysis and adjust for survey design to identify common configurations.I find four primary care configurations.In the most common configuration (42%), partners provided all assistance and most often helped with household chores.Conversely, children often assisted with transportation while sharing other responsibilities with paid caregivers.Older adults with limited physical capacity, probable dementia, and Medicaid were most likely to belong to the only configuration featuring paid caregiving.By studying the full population of older adults who receive care assistance, this study provides a broad perspective on how older adults rely on unpaid kin caregivers and when they rely on paid caregiving.In particular, it identifies transportation assistance as an overlooked form of caregiving and demonstrates that children are most frequently dividing responsibilities with other kin or paid caregivers.

SESSION 7480 (POSTER)
resident-level interventions to prevent falls and fall-related injury (FRI), system-focused interventions are also necessary to adequately address fall prevention.Relatively little attention has been given to research involving systems-focused interventions.We aimed to synthesize studies on the effects of system-focused interventions for fall and FRI prevention in LTCFs with the goal of identifying promising strategies and gaps.We searched Medline, CINAHL, and EMBASE databases from 2017 to 2022 following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guideline.We conducted a narrative synthesis to summarize included studies.In the initial review, 301 studies were identified and underwent title and abstract screening resulting in 98 articles were retrieved for full-text review.

ARTIFICIAL INTELLIGENCE IN MENTAL HEALTH FOR THE AGING POPULATION: A SCOPING REVIEW
Duo Helen Wei 1 , Bianca Hernandez 2 , and David Burdick 1 , 1. Stockton University, Galloway, New Jersey, United States, 2. Rutgers University, Galloway, New Jersey, United States Artificial intelligence (AI) has significant potential in mental health research and service delivery for the aging population.This poster provides an overview of its evolving utilization, including research methods, data sources, and predictors of mental health.The review of 26 articles from 2015 to 2023 found that surveys/questionnaires, clinical data, non-clinical data, and electronic health records are the most commonly used data sources, with clinical data being the most common, accounting for more than 50% of the articles.The frequently utilized AI techniques in mental health research for older adults were supervised learning (65.4%), deep learning (42.3%), unsupervised learning (7.7%), NLP (7.7%), and semi-supervised learning (3.8%).Mental health factors that received the most attention in the research were anxiety/depression (65.4%), dementia (Alzheimer's) (38.5%), mild cognitive impairment (23.1%), suicidal ideation (15.4%), and trajectory of cognitive decline (7.7%).AI is currently employed in various aspects of mental health research for older adults, including identifying and diagnosing mental health conditions at an early stage, monitoring mental well-being, assessing the severity of mental health conditions, predicting cognitive trajectories, and providing assistance in treatment.The recent application of generative language models, such as ChatGPT, has further advanced the field of AI in mental health research.However, ethical concerns such as bias, privacy, and transparency must be addressed before widespread implementation.In conclusion, AI has great potential as well as risks and ethical concerns for mental health research and service delivery to aging populations, but further research is needed.

ATTITUDES TOWARD AGING: EDUCATIONAL INTERVENTIONS FOR MEDICAL STUDENTS
Bronwyn Keefe, Boston University, Boston, Massachusetts, United States Ageism is stereotyping, prejudice, and discrimination based on a person's age, and in healthcare includes the implicit biases of providers.Like many other forms of prejudice, ageism contributes to health disparities and differences in health outcomes.The implicit and explicit biases of clinicians can impact clinical practice, leading to medical decisions based on stereotypes and prejudices.There is inconclusive evidence on undergraduate and graduate medical learners' attitudes towards aging.Some studies have found that learners hold negative associations with aging, while others have found a mix of positive and negative views toward caring for older adults.Educational interventions and intergenerational contact are two important strategies for increasing awareness and reducing ageism.Hence, it is important to discuss ageism with students and create programs and curricula to combat ageism to ensure the best care is provided to all of our patients.This session will present results from an interactive online module designed for 4th-year medical students that explores ageism and bias.Students completed the Expectations Regarding Aging Survey prepost.Preliminary results at pre-test show that 70% believe that as they get older, they will be more forgetful and 75% believe the human body is like a car: when it's old, it gets worn out.Healthcare professionals need to understand the existing gaps in health equity that our older adult patients experience that contribute to ageist attitudes.By drawing attention to professionals' implicit and explicit biases towards aging throughout medical training, we are able to help older adults receive better care.
Fifteen studies were included in data extraction.Fall prevention interventions evaluated in LTCF settings include: (1) multicomponent fall prevention programs, (2) staff education programs, (3) training in safe handling/transfers, (4) environmental adaptations, (5) electronic health record algorithms and prompts, (6) local culture change, (7) video analysis of falls to change local interventions, and (8) facilitylevel physical activity programs.About half of reported studies reported significant effects of system-focused interventions to reduce fall and FRI in LTCFs.Multicomponent fall prevention programs and physical activity interventions are the most effective systems-level interventions, while electronic health record interventions were the least effective.Overall, little attention has been given in the literature to evaluation of environmental adaptations at the systems level.Abstract citation ID: igad104.2598